from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-13 14:07:14.310921
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Sun, 13, Dec, 2020
Time: 14:07:17
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.6730
Nobs: 139.000 HQIC: -44.8009
Log likelihood: 1482.23 FPE: 1.61756e-20
AIC: -45.5730 Det(Omega_mle): 8.65602e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.481400 0.172945 2.784 0.005
L1.Burgenland 0.129395 0.084292 1.535 0.125
L1.Kärnten -0.297579 0.071528 -4.160 0.000
L1.Niederösterreich 0.086219 0.203914 0.423 0.672
L1.Oberösterreich 0.300811 0.170323 1.766 0.077
L1.Salzburg 0.180603 0.086291 2.093 0.036
L1.Steiermark 0.098978 0.121604 0.814 0.416
L1.Tirol 0.165312 0.080979 2.041 0.041
L1.Vorarlberg -0.003815 0.078270 -0.049 0.961
L1.Wien -0.120235 0.163226 -0.737 0.461
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.526005 0.217018 2.424 0.015
L1.Burgenland 0.002257 0.105773 0.021 0.983
L1.Kärnten 0.336769 0.089756 3.752 0.000
L1.Niederösterreich 0.123397 0.255879 0.482 0.630
L1.Oberösterreich -0.192551 0.213727 -0.901 0.368
L1.Salzburg 0.197738 0.108281 1.826 0.068
L1.Steiermark 0.227511 0.152594 1.491 0.136
L1.Tirol 0.149331 0.101615 1.470 0.142
L1.Vorarlberg 0.201322 0.098216 2.050 0.040
L1.Wien -0.553319 0.204822 -2.701 0.007
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.309820 0.074899 4.137 0.000
L1.Burgenland 0.102109 0.036505 2.797 0.005
L1.Kärnten -0.017889 0.030977 -0.577 0.564
L1.Niederösterreich 0.121346 0.088311 1.374 0.169
L1.Oberösterreich 0.276886 0.073763 3.754 0.000
L1.Salzburg -0.006025 0.037371 -0.161 0.872
L1.Steiermark -0.039917 0.052664 -0.758 0.448
L1.Tirol 0.090642 0.035070 2.585 0.010
L1.Vorarlberg 0.130395 0.033897 3.847 0.000
L1.Wien 0.040005 0.070690 0.566 0.571
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.199309 0.086965 2.292 0.022
L1.Burgenland -0.007551 0.042386 -0.178 0.859
L1.Kärnten 0.027103 0.035967 0.754 0.451
L1.Niederösterreich 0.037644 0.102537 0.367 0.714
L1.Oberösterreich 0.386314 0.085646 4.511 0.000
L1.Salzburg 0.093351 0.043391 2.151 0.031
L1.Steiermark 0.199208 0.061148 3.258 0.001
L1.Tirol 0.034622 0.040720 0.850 0.395
L1.Vorarlberg 0.104657 0.039358 2.659 0.008
L1.Wien -0.075658 0.082077 -0.922 0.357
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.642287 0.185616 3.460 0.001
L1.Burgenland 0.072758 0.090468 0.804 0.421
L1.Kärnten -0.010432 0.076768 -0.136 0.892
L1.Niederösterreich -0.077586 0.218854 -0.355 0.723
L1.Oberösterreich 0.115556 0.182802 0.632 0.527
L1.Salzburg 0.039961 0.092613 0.431 0.666
L1.Steiermark 0.120948 0.130514 0.927 0.354
L1.Tirol 0.233194 0.086912 2.683 0.007
L1.Vorarlberg 0.027394 0.084005 0.326 0.744
L1.Wien -0.144779 0.175185 -0.826 0.409
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.178995 0.128025 1.398 0.162
L1.Burgenland -0.039584 0.062399 -0.634 0.526
L1.Kärnten -0.007043 0.052949 -0.133 0.894
L1.Niederösterreich 0.187835 0.150950 1.244 0.213
L1.Oberösterreich 0.380743 0.126084 3.020 0.003
L1.Salzburg -0.030118 0.063878 -0.471 0.637
L1.Steiermark -0.033327 0.090019 -0.370 0.711
L1.Tirol 0.194599 0.059946 3.246 0.001
L1.Vorarlberg 0.040246 0.057940 0.695 0.487
L1.Wien 0.136937 0.120830 1.133 0.257
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.202519 0.162798 1.244 0.214
L1.Burgenland 0.078640 0.079346 0.991 0.322
L1.Kärnten -0.068884 0.067331 -1.023 0.306
L1.Niederösterreich -0.057415 0.191950 -0.299 0.765
L1.Oberösterreich -0.105478 0.160329 -0.658 0.511
L1.Salzburg 0.006576 0.081228 0.081 0.935
L1.Steiermark 0.391783 0.114469 3.423 0.001
L1.Tirol 0.527611 0.076227 6.922 0.000
L1.Vorarlberg 0.231309 0.073677 3.139 0.002
L1.Wien -0.203890 0.153649 -1.327 0.185
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.095019 0.188419 0.504 0.614
L1.Burgenland 0.034254 0.091834 0.373 0.709
L1.Kärnten -0.081443 0.077927 -1.045 0.296
L1.Niederösterreich 0.180348 0.222159 0.812 0.417
L1.Oberösterreich 0.027812 0.185562 0.150 0.881
L1.Salzburg 0.217051 0.094012 2.309 0.021
L1.Steiermark 0.175298 0.132485 1.323 0.186
L1.Tirol 0.067936 0.088224 0.770 0.441
L1.Vorarlberg 0.031100 0.085273 0.365 0.715
L1.Wien 0.263448 0.177830 1.481 0.138
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.601842 0.103534 5.813 0.000
L1.Burgenland -0.015092 0.050462 -0.299 0.765
L1.Kärnten 0.000574 0.042820 0.013 0.989
L1.Niederösterreich -0.042435 0.122074 -0.348 0.728
L1.Oberösterreich 0.287313 0.101964 2.818 0.005
L1.Salzburg 0.008204 0.051658 0.159 0.874
L1.Steiermark 0.016995 0.072799 0.233 0.815
L1.Tirol 0.071792 0.048478 1.481 0.139
L1.Vorarlberg 0.177109 0.046857 3.780 0.000
L1.Wien -0.098722 0.097716 -1.010 0.312
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.109699 -0.008904 0.197991 0.244362 0.033215 0.070097 -0.132439 0.135805
Kärnten 0.109699 1.000000 -0.053016 0.184295 0.110237 -0.153349 0.179987 0.011800 0.267478
Niederösterreich -0.008904 -0.053016 1.000000 0.242418 0.052940 0.189072 0.090635 0.038564 0.365660
Oberösterreich 0.197991 0.184295 0.242418 1.000000 0.259648 0.258593 0.069913 0.051962 0.058373
Salzburg 0.244362 0.110237 0.052940 0.259648 1.000000 0.133142 0.042645 0.077411 -0.049029
Steiermark 0.033215 -0.153349 0.189072 0.258593 0.133142 1.000000 0.083693 0.068804 -0.170466
Tirol 0.070097 0.179987 0.090635 0.069913 0.042645 0.083693 1.000000 0.133928 0.107969
Vorarlberg -0.132439 0.011800 0.038564 0.051962 0.077411 0.068804 0.133928 1.000000 0.063079
Wien 0.135805 0.267478 0.365660 0.058373 -0.049029 -0.170466 0.107969 0.063079 1.000000